Machine Learning Engineering, Intern

Reposted One Month Ago
Toronto, ON, CAN
Hybrid
50-65 Hourly
Internship
Fintech • Software • Financial Services
The Role
Work on designing, training, and deploying scalable ML models for credit risk, fraud detection, and personalization. Build ML pipelines for low-latency inference, automate experimentation and hyperparameter tuning, support full ML lifecycle (feature engineering, evaluation, A/B testing, monitoring for drift), and experiment with deep learning and reinforcement learning techniques while ensuring compliance and scalability in a fast-growing FinTech.
Summary Generated by Built In
About Bree

Bree is a consumer finance platform that brings better, faster, and cheaper financial services to over half the Canadian population who live paycheck to paycheck. We operate in a huge, but overlooked market in a country with the least amount of financial technology innovation in the developed world. Our first act is to become the cheapest and best provider of short-term credit to the 20 million people in Canada who live paycheck to paycheck.

More than 800,000 Canadians have already signed up with Bree and we believe we are just scratching the surface. We are in an exciting place where we have product market fit, explosive growth, and a clear path to becoming one of the most important FinTechs in Canada.

About the Role

We’re looking for a Machine Learning Engineering Intern to work alongside our ML, data, and infrastructure teams. You’ll contribute to real modelling and data problems, learn how production ML systems are evaluated and monitored, and use AI tools thoughtfully to move from experimentation to reliable implementation.

This is an 8-month co-op term.

At Bree, co-ops are full members of the Engineering team. You’ll work on the same customer and business problems as full-time engineers, ship real production work, and take part in design discussions, code reviews, testing, and releases. We pair that responsibility with close mentorship, clear context, and projects scoped for you to make a meaningful impact from day one.

What You'll Do
  • Help prepare, explore, and validate data used in models and analytical workflows.

  • Support training and evaluation of models used for areas such as credit risk, fraud detection, and customer experience.

  • Build scripts, tools, and tests that make experimentation and model evaluation more repeatable.

  • Learn how model performance is monitored in production, including data quality, drift, and operational reliability.

  • Explore new approaches with mentorship, then clearly document results, tradeoffs, and next steps.

What You'll Need

  • Currently enrolled in a Computer Science, Statistics, Engineering, Data Science, or related post-secondary programme, and available for the full 8-month term.

  • Strong Python foundations, plus experience working with data through coursework or projects. Familiarity with SQL, pandas, or similar tools is helpful.

  • Foundational knowledge of statistics and machine learning concepts, with coursework, research, personal projects, or competitions you can discuss.

  • Interest in tools such as PyTorch, LightGBM, or modern LLM workflows. Production ML experience is not required.

  • Curiosity, rigour, and strong communication. You enjoy investigating ambiguous problems, checking your assumptions, and learning from feedback.

Benefits

  • Compensation: $50-$70/hour, based on experience and interview performance

  • Offer Matching: We're open to matching competing offers

  • Perks: $250 monthly lunch stipend, bi-annual company retreat

  • Impact: Push to prod, with 10x the ownership and impact of typical roles

  • Growth: Mentorship programs and career training sessions

  • Path to Full-Time: Strong conversion opportunities for high performers

Skills Required

  • Professional experience building and deploying production ML systems and handling imbalanced datasets in high-stakes domains (finance or e-commerce).
  • Good understanding of traditional ML systems and modern deep learning and reinforcement learning architectures, with applied track record.
  • Experience with PyTorch and LightGBM (or similar ML frameworks).
  • Experience with full ML lifecycle: feature engineering, model evaluation, A/B testing, monitoring for drift, and hyperparameter tuning.
  • Architectural thinking and experience scaling ML systems while maintaining accuracy, fairness, and explainability.
  • Exceptional collaboration and communication skills; ability to explain complex ML concepts to non-technical stakeholders.
  • Competitive ML experience (e.g., top Kaggle rankings, NeurIPS challenges, open-source contributions).
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The Company
HQ: Quatro Barras
30 Employees
Year Founded: 2021

What We Do

Bree is a consumer finance platform in Canada, delivering better, faster, and more affordable financial services to the 20M+ people living paycheck to paycheck. Our flagship product is an interest-free liquidity solution that helps consumers in a crunch. More than 400,000+ consumers have already signed up with Bree since our founding in 2021. We are backed by Y-Combinator (S21) and several other VCs + angels.

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